Suicide is a major public health concern and the second-leading cause of death among young adults in the U.S. Predicting who is at risk for suicide has been difficult, but new research suggests that suicidal individuals could be identified by signatures of brain activity.

A research team used functional magnetic resonance imaging and machine learning algorithms to assess the neural representation of specific emotional concepts. The study was published in Nature Human Behaviour in October.

The researchers used data from 33 participants to train a machine-learning system to spot differences in networks of brain activity in response to words related to death, positive concepts, or negative concepts. Then they tested it on brain images from both people who had suicidal thoughts and those who did not.

Based on the brain representations of six concepts (“death,” “carefree,” “good,” “cruelty,” “praise” and “trouble”), the machine-learning algorithm was able to identify with 91 percent accuracy whether a participant was in the control or suicidal group. Within the group of people with suicidal thoughts, the system could distinguish with 94 percent accuracy those people who had made a suicide attempt from those who had not.

The results need to be validated in larger samples, but the researchers are hopeful that the approach could one day be used to identify and monitor suicide risk.